The distribution of the chromosomes or DNA in the nuclei of cells can be quantitatively measured using a computer and image analysis techniques. Moreover, these measurements or features can be used to detect both malignancy associated changes (MACs), and changes during neoplasia. The features that appear to have the most discriminatory power are texture features. Such features quantitatively describe the intensity variation of the chromatin (the substance that constitutes the chromosomes or DNA and is readily visualized by staining) in the nucleus of a cell. The most widely used chromatin texture features are based on a statistical or probabilistic assessment of the gray-levels (intensity levels or optical density levels) in the cell nucleus. Unfortunately these features are difficult to relate to the terms and adjectives (e.g. granularity, compaction, margination, clumping, chromatin particles, condensation) used by cytologists to describe chromatin texture. Moreover they are usually defined at the pixel level and therefore fail to take into account the structural aspect of the chromatin distribution; e.g. this is true of all but the discrete texture features described in the United States Patent of Palcic et al. [System and method for automatically detecting malignant cells and cells having malignancy-associated changes; U.S. Pat. No. 6,026,174 dated Feb. 15, 2000]. Therefore their efficacy in relation to quantifying chromatin distribution is questionable.
An alternative approach to computing chromatin texture features is to segment the chromatin into aggregates and then to synthesize chromatin features from quantitative features computed for these aggregates. This approach has two advantages: (i) the segmentation step introduces structural information, and (ii) the synthesized features can be related to qualitative descriptions of chromatin texture made by cytologists. The key to this approach is the segmentation step. Several different methods of chromatin segmentation have been published in the literature. A characteristic they have in common is that they require the a priori specification of one or more operational parameters such as threshold values and region merging criteria. Moreover these parameters need to be tuned to the particular application. As a consequence these methods are not robust to changes in, or non-uniformity of, illumination and staining. The quality of the segmentations produced is arguably poor. This in turn affects the quality of the chromatin features that are computed from such segmentations. This is likely one of the major reasons that such features, with the exception of those of Young, Verbeek, and Mayall [Characterization of chromatin distribution in cell nuclei; Cytometry; vol. 7; 1986; pp. 467-474], have not found widespread use. Another possible reason is that the software implementation of the segmentation step is complicated.
Three existing methods of chromatin segmentation deserve special attention. The first is the method of Young, Verbeek, and Mayall because it is the basis of the discrete texture features detailed in the aforementioned United States Patent of Palcic et al. The second is the method of Wolf, Beil, and Guski [Chromatin structure analysis based on a hierarchic texture model; Analytical and Quantitative Cytology and Histology; vol. 17; no. 1; 1995; pp. 25-34] because it utilizes the watershed transform as does the preferred embodiment of the present invention. The third is the method of Kondo and Taniguchi [Evaluation of the chromatin for cell images; Systems and Computers in Japan; vol. 17; no. 9; 1986; pp. 11-19] because it represents the closest known prior art to the present invention.
The Young, Verbeek, and Mayall (YVM) method of chromatin segmentation takes as input a digitized image of a cell nucleus visualized by light microscopy. Young, Verbeek, and Mayall illustrate their method on images obtained from foam cells in human nipple aspirate fluid, and rat urothelial cells. The method of staining is unspecified. The YVM chromatin segmentation method involves nothing more than partitioning the gray-level histogram of the nucleus image into three parts (i.e. choosing two gray levels), and then using this division to label each nucleus pixel. The result is a segmentation comprising regions of low, medium, and high optical density. The manner in which the partition points (threshold values) are determined must be a priori specified. Although the YVM segmentation method is simple (hence its relative popularity) the quality of the segmentation is questionable for the following two reasons. Firstly, the method of segmentation utilizes only the intensity histogram and does not take into account any spatial information. Secondly, the method requires the specification of two threshold values. The manner in which these are chosen must be a priori specified. Moreover they must be tuned to the particular application.
The Wolf, Beil, and Guski (WBG) method of chromatin segmentation takes as input a digitized image of a cell nucleus, visualized by light microscopy, from a cervical tissue section obtained by colposcopic biopsy and stained by the Feulgen method. The WBG method consists of two steps. The first step involves determining the watershed of the gradient of the input image. This is done using a modification of the classic watershed algorithm of Vincent and Soille [Watersheds in digital spaces: an efficient algorithm based on immersion simulations; IEEE Transactions on Pattern Analysis and Machine Intelligence; vol. 13; no. 6; 1991; pp. 583-598]. The result is an oversegmentation; i.e. too many regions are delineated and as a consequence the result does not correspond very well to the chromatin patches in the original image. The second step involves selectively merging the regions segmented in the first step. Specifically, this step involves fitting a plane to each segmented region using standard least-squares techniques and then iteratively merging neighboring regions based on merging criteria related to the standard deviation of gray-levels in the regions. The decision to merge two regions is based on the evaluation of a single parameter that is then compared to a threshold value. This is a drawback of the method because the manner in which this threshold value is determined must be a priori specified.
The Kondo and Taniguchi (KT) method of chromatin segmentation represents the closest known prior art to the present invention. The method takes as input a digitized image of a cell nucleus, visualized by light microscopy, from a Pap smear. The method comprises three steps: (i) local maxima (with respect to optical density) are located in the input image (these correspond to local minima of intensity), (ii) the input image is partitioned into sub-images (regions), each containing a single maximum, and (iii) a chromatin granule (densely stained blob of chromatin) is segmented from each region in turn using local adaptive thresholding. Kondo and Taniguchi propose three different methods for the partitioning step: (i) partitioning using a Voronoi neighborhood, (ii) region partitioning by directed tree, and (iii) area expansion by difference direction. A drawback of the Voronoi neighborhood method is that it does not use the topography of the input image to determine a region around each minimum. Consequently it is possible that the region determined around a minimum cuts through one or more adjacent chromatin particles. A drawback of the directed tree method is that it is necessary to a priori select a sensitivity parameter to control growth. A drawback of the density difference method is that the growth is not prescribed by geodesic distance (i.e. if the image is viewed as a landscape then the growth is not prescribed by the topography of the landscape). The local adaptive thresholding method of segmenting a granule from each region has several potential drawbacks including sensitivity to noise and non-uniform illumination, and the need to prescribe the manner in which the threshold value is determined.
The present invention is specifically designed for the purpose of segmenting chromatin particles in the nucleus of a cell. The method takes as input an image of the nucleus of a cell. Consequently the task of segmenting a cell from a field of cells and the task of segmenting the nucleus from a single cell, are not the subjects of this invention. Indeed details of these tasks are described in International Patent Application number PCT/AU01/00787 (WO 02/03331) and co-pending International Patent Application number PCT/AU99/00231 (WO 99/52074) respectively.